Organizing memories for generalization in complementary learning systems
Memorization and generalization are complementary cognitive processes that jointly promote adaptive behavior. For example, animals should memorize a safe route to a water source and generalize to features that allow them to find new water sources, without expecting new paths to exactly resemble previous ones. Memory aids generalization by allowing the brain to extract general patterns from specific instances that were spread across time, such as when humans progressively build semantic knowledge from episodic memories. This cognitive process depends on the neural mechanisms of systems consolidation, whereby hippocampal-neocortical interactions gradually construct neocortical memory traces by consolidating hippocampal precursors. However, recent data suggest that systems consolidation only applies to a subset of hippocampal memories; why certain memories consolidate more than others remains unclear. Here we introduce a novel neural network formalization of systems consolidation that highlights an overlooked tension between neocortical memory transfer and generalization, and we resolve this tension by postulating that memories only consolidate when it aids generalization. We specifically show that unregulated memory transfer can be detrimental to generalization in unpredictable environments, whereas optimizing systems consolidation for generalization generates a high-fidelity, dual-system network supporting both memory and generalization. This theory of generalization-optimized systems consolidation produces a neural network that transfers some memory components to the neocortex and leaves others dependent on the hippocampus. It thus provides a normative principle for reconceptualizing numerous puzzling observations in the field and provides new insight into how adaptive behavior benefits from complementary learning systems specialized for memorization and generalization.